2021/09/08 by Hanxiang Hao, Hao, Hanxiang, Sriram Baireddy +13
Computer Science · Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote-Sensing Image Classification #Satellite Image Processing and Photogrammetry #cs.CV
paper · pdf · doi:10.48550/arxiv.2109.03961
This is an extended version of our ACM SIGSPATIAL'21 conference paper
arxiv created 2021/09/08 · openalex publication_date 2021/09/08 · arxiv updated 2021/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic building segmentation is an important task for satellite imagery analysis and scene understanding. Most existing segmentation methods focus on the case where the images are taken from directly overhead (i.e., low off-nadir/viewing angle). These methods often fail to provide accurate results on satellite images with larger off-nadir angles due to the higher noise level and lower spatial resolution. In this paper, we propose a method that is able to provide accurate building segmentation for satellite imagery captured from a large range of off-nadir angles. Based on Bayesian deep learning, we explicitly design our method to learn the data noise via aleatoric and epistemic uncertainty modeling. Satellite image metadata (e.g., off-nadir angle and ground sample distance) is also used in our model to further improve the result. We show that with uncertainty modeling and metadata injection, our method achieves better performance than the baseline method, especially for noisy images taken from large off-nadir angles.